Factors for allowing an automobile to run safely can include a tire pneumatic pressure. A pneumatic pressure lower than an appropriate value may deteriorate a stable operation or fuel consumption to thereby cause a tire burst. Thus, a tire pressure monitoring system (TPMS) for detecting a decreased tire pneumatic pressure to issue an alarm to a driver to prompt an appropriate procedure is an important technique from the viewpoints of environment protection and driver safety.
Conventional alarm apparatuses can be classified into a direct detection type (direct TPMS) and an indirect detection type (indirect TPMS). The direct TPMS includes a pressure sensor provided in a tire wheel to directly measure a tire pneumatic pressure. This direct TPMS can accurately detect a decreased pneumatic pressure. The direct TPMS on the other hand requires exclusive wheels and is involved with a disadvantageous fault tolerance performance in an actual environment, thus leaving technical and cost disadvantages.
On the other hand, the indirect TPMS uses a method to estimate a pneumatic pressure based on rotation information of tires. The indirect TPMS can be further classified into a dynamic loaded radius (DLR) type, a resonance frequency mechanism (RFM) type, and a global positioning system (GPS) type for example. Among them, the DLR type apparatus used in the present invention uses a phenomenon in which a tire rolling under a load has a reduced tire radius (dynamic loaded radius) due to a decreased pressure and thus is rotated at a higher speed than a tire having a normal pressure. Thus, the DLR type apparatus makes a relative comparison among the rotation speeds of four tires to thereby detect a decreased pressure. Since the DLR type apparatus can subject only the wheel rotation speed signals obtained from wheel speed sensors to a relatively-simple calculation processing, the DLR type apparatus has been widely researched mainly for the purpose of detecting the puncture of one wheel.
In the case of the DLR method however, a difference in a wheel speed may be caused also by general running conditions such as a vehicle turning or acceleration and deceleration. Thus, the DLR method has a disadvantage that a decreased pressure cannot be accurately detected through all running statuses. For example, when a vehicle turns to the left, the entire vehicle is inclined downwardly to right side due to the centrifugal force. Thus, the right wheels have reduced dynamic loaded radii, which causes a false alarm.
In order to avoid the disadvantages as described above, Patent Literature 1 for example uses a processing for example according to which information obtained from sensors is not used as data to determine a decreased pressure in cases other than a reference running condition where the vehicle is running straight on a flat road at a fixed speed (e.g., a case where acceleration applied to the vehicle in a front-and-rear direction or in a lateral direction is equal to or higher than a predetermined value).
The tire radius also changes depending on the weight of a person in the vehicle, the number of passengers therein, the weight of baggage, or the place where the baggage is placed. Rear wheel tires in particular bear a significantly-fluctuated load since heavy baggage is frequently placed in the trunk. This is disadvantageous when comparison is made between two front wheels and two rear wheels. Thus, in order to improve the estimation accuracy of an alarm system for a tire having a decreased pneumatic pressure based on the DLR method, there is required a processing to correct a decreased pressure determination value based on information regarding a vehicle mass, for example.
Known methods for obtaining a load of a running vehicle include a method of attaching a load sensor to the vehicle to measure a load provided in the vehicle and a method of converting a suspension stroke amount or a change amount of a vehicle height to a load. However, the direct methods as described above require an additional measurement apparatus or a special vehicle structure. Thus, these methods cannot be realized in a general vehicle due to technical and cost reasons.
There may be considered another method that does not require a special sensor for example in the vehicle and that estimates the load status of the vehicle. Specifically, there is provided a method of obtaining a vehicle acceleration a or signals of torques F applied to the respective wheels from a wheel speed sensor provided in an anti-lock braking system (ABS) to use the relation of the motion equation F=ma to calculate the vehicle mass m as an inclination when a and F are subjected to linear regression (Japanese Patent Application No. 2009-097882 filed by the present applicant). When the vehicle contact area has a gradient, an influence by the gravity is a problem. To solve this, there has been known a method to correct an estimation value based on the gradient information obtained from a gyro sensor or GPS (see Patent Literature 2 for example).
The method as described above can estimate the vehicle mass at a certain level of accuracy and with a low cost. Thus, this method has been used as an ancillary technology for an alarm system for a tire having a decreased pneumatic pressure based on the DLR. However, this method had disadvantages as described below. Specifically, in order to process the linear regression in a real-time manner, a regression coefficient has been conventionally calculated by a Kalman filter (iterative least squares technique). However, this algorithm assumes white data and thus is not always robust to a colored outlier observed suddenly. Thus, an estimation value may be unstable depending on the running conditions. Furthermore, when an outlier occurs frequently, a delay is caused in the convergence of estimation values. To prevent this, such data that is expected to have an adverse effect on the current estimation value must be subjected to an adaptive processing to assume such data as an outlier to thereby reject the data for example. This method is generally called a “robust Kalman filter” for which a status estimation method has been suggested according to which robustness to outliers is improved by incorporating previous knowledge derived from a gamma distribution into calculation of a Kalman gain for example (see Non-Patent Literature 1 for example).